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Filterbank-Based Fingerprint Matching

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					            Fingerprint Matching
                     Chapter 4, sections 4.4-4.8
                  Handbook of fingerprint recognition

                                 &
    Filterbank-Based Fingerprint
              Matching
Jain A.K. Prabhakar S., Jonh L. and Pankanti S., “IEEE Trans. On Image
                   Processing”, vol. 9, No. 5, 2005.




                  Alireza Tavakkoli
                    Outline
• Fingerprint Matching     • Filterbank-based
  – Global vs Local          Matching:
    Minutiae Matching.       – Motivation
  – Dealing with             – Filter-based feature
    Distortion.                extraction:
  – Ridge Feature-based         • Reference point
    Matching Techniques.          location.
                                • Filtering
  – Comparing the
                                • Feature vectors
    Performance.
                             – Matching
                             – Experimental results
                                                      2
               Global vs. Local
              Minutiae Matching
• Trade offs:
  – Simplicity, low cost, high distortion tolerance.
  – High distinctiveness.


• [Hrechak and McHugh (1990)]:
  – Eight dimensional feature vector:
  – Minutiae: dots, ridge endings, ridge vi1 , vi 2 ,, vi 8 
                                      vi bifurcations,
    islands, spurs, crossovers, bridges and short ridges.
  – Invariant to fingerprint alignments.
  – Practical applicability!!!!!!

                                                                   3
                     Global vs. Local
                    Minutiae Matching
• Chen and Kuo (1991), Wahab, Chin and Tan (1998):
   – Enriched local structures proposed by Harchak and McHugh in 1990.
       •   Distance
       •   The ridge count
       •   Relative orientation of each surrounding minutiae with the central one.
       •   Angle between orientation of the line connecting each minutiae to central
           one and its orientation.
   – Comparing local structures by correlation or tree-matching.
• Fan, Liu and Wang(2000): (Geometric Clustering)
   – Each cluster  rectangular bonding box.
   – Using a fuzzy bipartite weighted graph matching.
• Willis and Myers (2001):
   – Minutiae counting in a dart board pattern of wedges and ridges.
   – Partially invariant to rotation and translation.


                                                                                       4
                      Global vs. Local
                     Minutiae Matching
•   [Jiang and Yau, Ratha et. al. (2000)]: (Using both methods advantages)
     – Fast local matching for recovering alignments.
     – Consolidation stage.
                  Jiang                                                            Ratha




                                                                Vi  m j | sd m i , m j   d max
                                              Si   i , Ei   
                                                                 Ei  eij | i, j , d m i , m j , rcm i , m j , ij 
                                                    V


                                                                                                                     5
  Variants of the 2 Stage Algorithm
• Zhang and Wang (2002):                 • Lee, Choi and Kim
   – Using Core points
       •    speed up the initial local
                                           (2002):
           structure matching.             – Using more minutiae
                                             pairs:
                                              • Guide the
                                                consolidation step.
                                              • Robustness
                                           – Normalization.



                                                                      6
     More Local Minutiae Matching
              Methods
• Maio and Maltoni (1995) and Kovac-Vajna (2000):
   – Enhancement and accurate minutiae extraction only on template.
   – Extraction of minutiae template T.
   – Locally checking correspondence in verification stage.




• Maio:
   – Gray level minutiae extraction.
   – Locally tracking the ridges in verification for finding correspondence.




                                                                               7
                  Kovac Algorithm
• Kovac:
   – 16x16 neighborhood of minutiae in T
     correlated by I  list of candidate
     positions.
   – Triangular matching:
       • Start with 2 minutiae in T and
         candidate positions in I.
       • Expanding the list by adding a pair
         of minutiae and candidate.
   – Consolidation:
       • Checking the correspondence of
         gray scale profiles between every
         pair.
       • 1) Ridge count.
       • 2) Dynamic time warping (Handle
         small perturbations).


                                               8
       Dealing with Distortion
• One of the most critical intra-class
  variability. (NIST 24)
  – Mechanical force sensor  less distortion
  – Automatic detection of distortion from videos.


• Distortion-tolerant matchers:
  – Both of the above solutions are difficult to
    implement in commercial sensing systems.

                                                     9
   How to deal with distortion?
• Relaxing spatial relationships between minutiae:
  – Global matching techniques:
     • Tolerance boxes (spheres):
         – High distortion  larger Boxes  high false match
  – Polar coordinate boxes (Jain (97) and Luo (2000)):
     • Edit distance for matching pre-aligned minutiae.
     • Size of boxes increase by the distance from center.
  – Kovac method:
     • Triangular matching can tolerate large global distortions.
     • Adding this small differences may be large!!!!
• Non of the above explicitly address the problem!
                                                                    10
      Dealing with Distortion
• Almansa and Cohen (2000):
  – A 2D warping algorithm (mapping FP
    patterns):
    • Controlling warping by minimizing and energy
      function.
       – Two minutiae spatially coincide.
       – Penalty term  increasing by the irregularity of the
         warping.
    • Two step iterative algorithm to minimize energy.
  – Problem with convergence!!!
                                                                11
       Dealing with Distortion
• Bazen and Gerez (2002):
  – Smoothed mapping between template and input
    minutiae.
  – Algorithm:
     • Initially computing minutiae through a local approach and
       consolidation step.
     • Reduction of the size of tolerance box
         – Use of a thin spline model to deal with non-linear distortion.
     • Locally moving minutiae in input image to best fit the
       template minutiae, iteratively. (According to the model
       smoothness constrains)
  – Significant improvements achieved.

                                                                            12
Normalization to canonical form
   Senior and Bolle (2001)




                                  13
    Normalization Techniques
• Lee Chi and Kim (2002):
  – Normalization during the matching stage:
    • Normalization according to local ridge frequency.
    • Distortion  increase in distance between
      minutiae  local ridge frequency decreases 
      Normalization can compensate for that.
  – Problem:
    • Far apart ridges  normalization may have higher
      distortion errors than the distortion itself.


                                                          14
Modeling Skin Distortion [Maio]




                              15
Distortion Recovery




                      16
    Ridge Feature-based Matching
            Techniques
• Why?
    – Difficulty in reliable minutiae extraction from poor quality images.
    – Time consuming.
    – Use of additional features increases the accuracy and robustness.


• Alternative features:
    –   Size and silhouette.    (unstable)
    –   Singularities.          (unstable)
    –   Spatial relationship.   (tree grammars, incremental graph matching)
    –   Shape features.         (1D signature from 2D, used with minutiae-based)
    –   Global/local texture.   (Texture properties from ridge lines)
    –   Sweat pores.            (Very discriminative but expensive)
    –   Fractal features.



                                                                               17
     Fingerprint Texture Analysis
• Analyzing texture in furrier domain:       (Coetzee and Botha (93) and Willis and
   Myers (2001))
    – Spatial fingerprint texture Almost constant in frequency domain.
    – Small deviations from the dominant frequency  minutiae!!
    – Wedge-ring detector.
        • Accumulating the harmonic of individual regions.
    – Global texture analysis  all regions into one measurement  Loss of
      spatial information.


• Filterbank-based Analysis of Fingerprint: (Jain (2000))
    – Topic of next talk (!).




                                                                                  18
        Comparing Performance
• Various fingerprint matching techniques.
    – Which one is the best algorithm?

• Performance involves a Trade off among different measures.

• Performance relates to difficulty of the benchmark  lack of a global
  one.

• Before FVC NIST Databases  not good for live-scan.
    – NIST 4, 10, 14: Rolled inked impressions.
    – NIST 24       : Videos.
    – NIST 27       : Latent fingerprints.

• FVC2000/02       : (can be found on the DVD of the book)


                                                                     19
            Typical Mistakes
• Using the same datasets for trainig, validation
  and testing.
• Computing performance on very small dataset.
• Cleaning the dataset by removing rejected or
  misclassified samples.
• Claiming better classification while using
  different datasets.
• Hiding the weak points of an algorithm/
  Documenting its failures.

                                                    20
        Second Talk


        Filterbank-Based
       Fingerprint Matching
  Jain A.K. Prabhakar S., Jonh L. and Pankanti S.
“IEEE Trans. On Image Processing”, vol. 9, No. 5, 2005.




                                                          21
                   Outline
• Motivation
• Filter-based feature extraction:
  – Reference point location.
  – Filtering
  – Feature vectors
• Matching
• Experimental results

                                     22
              Introduction
• Extraction and explicit detection of
  complete ridge structures!???
• Use of components of rich discriminatory
  information.
• Local ridge structures.
• Matching fingerprints with different number
  of registered minutiae.

                                            23
                           Overview
• Single reference point:
    – Assuming the vertical alignment.
    – Rotation invariance can be achieved by a cyclic rotation of the extracted
      feature values.

• Tessellation of region of interest around reference point.

• Filtering the region of interest in 8 direction using Gabor filter-banks.

• Computation of the Average Absolute Deviation (AAD) of gray
  values in each sector.

• Generation of the “Finger Code”.



                                                                             24
Overview




           25
     Reference Point Location
• Using conspicuous landmarks to locate
  reference point.
  – Point of maximum curvature of concave ridges.




                                                    26
 Reference Point Location (Contd.)
• Multiple resolution analysis of orientation map:
  – Handling noise in poor quality images:
     • Using large neighborhoods.
  – Accurate localization:
     • Sensitive to local variations.




• Estimation of Orientation Field.


                                                     27
       Least Square Orientation
              Estimation
• Divide Image into wxw blocks.
• Compute gradient at each pixel.
• Estimate the local orientation at center of each
  block.




                                                     28
Reference Point Location Algorithm
• Estimate the orientation
  field described above.
• Smooth the orientation field
  in a local neighborhood:
   – Use a continuous vector field.


• Compute the sine
  component of the smoothed
  orientation field, (E)
• Initialize a label image, (A).


                                      29
Reference Point Location Algorithm
• For each pixel in the E, integrate the values of
  region RI and RII and compute:

  Ai, j    E i, j    E i, j 
             RI           RII




• Find maximum of A and assign its coordinate to
  core.
• Perform algorithm for a fixed number of times
  with less window sizes.
                                                     30
Localization of Core Point




                             31
Tessellation of Region of Interrest




                                      32
                   Filtering
• Gabor filters:
  – Remove noise.
  – Preserve true ridge and valley structures
  – Provide directional information.


• Minutiae:
  – Anomaly in local parallel ridges.


                                                33
                 Filtering Stages

• Normalization:



• Even Symmetric
  Gabor Filter:
  – Mask 32x32.
  – Ferq. = 1/k
  – Angels:   0 ,22.5 ,45 ,,157.5

                                           34
Filtering Results




                    35
           Feature Vector
• Average Absolute Deviation:
                                               
                         Fi  x, y   Fi
                  1
            Vi                                
                  ni    n                      
                        i                      




                                                    36
How Discriminatory?




                      37
                           Matching
• Euclidian distance.
   – Translation Invariance:
       • Reference Point
   – Rotation Invariance:
       • Approximated by cyclic rotation of Finger Codes.




• Generating 11.25 degree rotated image in registration
  stage.
                                                            38
                     Experiments
• Database 1: (MSU-DBI)
  –   167 subjects.
  –   Digital Biometrics’ optical sensor.
  –   Image size:        508x480
  –   35%                women.
  –   46.5%              under 25.
  –   50.51%             between 25 and 50.
  –   2.5%               older than 50.
  –   Two impressions taken from four finger.
  –   A second round of collection after 6 weeks.
  –   Total database size:        2672 images.
  –   Live feedback at collection time  well centered images.
  –   Distortion in data collected after 6 weeks  Challenging.

                                                                  39
               Experiments
• Database 2: (NIST 9 Vol. 1 CD 1)
  – 1800 images.
  – 900 different fingers.
  – 832x768




                                     40
                   Experiments
• MSU-DBI                        • NIST 9
  – Rejected: 100(4%)              – Rejected: 100 (5.6%)
     • Why?                           • Why?
        – Ref point at corner.           – The same reasons.
        – Poor Quality.
          (dryness)




                                                               41
Genuine and Imposter Probabilities




                                 42
Experiments




              43
ROC curve (MSU-DBI)




                      44
ROC Curve (NIST9)




                    45
              Observations
• Most of false accepts are among the same
  type.
  – Good for indexing.
• Captures the discriminatory information.
  – Good for combining with minutiae.


• Combination by Neyman-Pearson Rule.

                                             46
          Neyman-Pearson Rule
pX1 | wG , pX 2 | wG , pX1 | wI , pX 2 | wI 
pX1 , X 2 | wG   pX1 | wG   pX 2 | wG 
                                              Joint Probs
pX1 , X 2 | wG   pX1 | wG   pX 2 | wG 
Classifica tion :
                               
                              p X1 , X 0 | wG
                                 0
                                              
                                              
               w
                                             
                                       2
                          if
      X1 , X 0   G
       0
             2
                                  0    0
                              p X1 , X 2 | wI
                  wI
                         otherwise
                                             pX1 , X 2 | wG 
                                     1)   pX , X | w 
                     must satisfy :              1   2    I

                                     2)  0  2 p X1 , X 2 | wI dX1dX 2
                                              RG
                                         R 2  RG  RI2
                                                 2

                                                                         47
Questions?


             48

				
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